Medical imaging systems such as magnetic resonance imaging (MRI) are capable of producing exact cross-sectional image data that express the physical properties related to the human or animal body. Reconstruction of three-dimensional (3D) images using collections of parallel 2D images representing cross-sectional data has been applied in the medical field for some time.
Various rendering techniques are applied for 3D display of medical images. One such technique is surface rendering. A limitation of this technique is that it does not adequately visualize the tissue within solid organs; it is optimized for visualization of surfaces and boundaries.
Another rendering technique is volume-rendering. Instead of overlaying surfaces using a complex model of 3D data, volume-rendering relies on the assumption that 3D objects are composed of basic volumetric building blocks, so-called “voxels”. A voxel has a spatial coordinate and associated voxel values like signal intensity, transparency, assigned color, and so forth. By using a set of voxel data it is possible by volume rendering to provide an image of the body part of the patient under examination by various volume rendering techniques. In general, a “voxel” is a unit cube with a unit vector along the x-axis, a unit vector along the y-axis, and a unit vector along the z-axis of a three-dimensional image.
In connection with examination and quantitative analysis of the left ventricle of the heart, the so-called bull's-eye plot is a wide-spread and accepted analysis tool. In the article: “Integrated Visualization of Morphologic and Perfusion Data for the Analysis of Coronary Artery Disease” by S. Oeltze et al. published in Proc. EuroVis, pages 131-138, 2006, a visualization tool for the left ventricle of the heart is disclosed. In the disclosure the bull's-eye plot is extended to include a 3D-elevation profile of a parameter map overlayed onto to a bull's eye plot. However, even though quantitative analysis is related to the anatomy via the bull's-eye plot, the bull's-eye plot is an abstraction that does not directly reflect the anatomy of the left ventricle.
In general, despite of the availability of detailed 3D images of the body part under investigation it is still challenging for the clinical user to efficiently extract information from the data. The clinical user typically needs to inspect a plurality of cross-sections and 2D visualizations of the anatomy and the quantitative analysis data and combine these mentally. This leads to inefficient analysis and decreases the reproducibility of the diagnostic work flow. As the data size increases, there is an ever increasing need in the art for condensed and comprehensive visualization.
Hence, an improved method for visualizing a 3D image of a human or animal body part would be advantageous, and in particular a more efficient method that would improve the coupling between quantitative analysis data and anatomy.